code: def test_percentage_of_features_with_missing_values(sparse_data):
      X, y, categorical = sparse_data
      meta_features.metafeatures.set_value(
          "NumberOfFeaturesWithMissingValues",
          meta_features.metafeatures[
              "NumberOfFeaturesWithMissingValues"
          ](X, y, logging.getLogger("Meta"), categorical),
      )
      mf = meta_features.metafeatures[
          "PercentageOfFeaturesWithMissingValues"
      ](X, y, logging.getLogger("Meta"), categorical)
      assert mf.value == pytest.approx(0.0)
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 9 for calling a global function with example usage: test_percentage_of_features_with_missing_values(sparse_data) without return types